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Development of a Fusion Vegetation Index Using Full-PolSAR and Multispectral Data
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 Title & Authors
Development of a Fusion Vegetation Index Using Full-PolSAR and Multispectral Data
Kim, Yong-Hyun; Oh, Jae-Hong; Kim, Yong-Il;
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The vegetation index is a crucial parameter in many biophysical studies of vegetation, and is also a valuable content in ecological processes researching. The OVIs (Optical Vegetation Index) that of using multispectral and hyperspectral data have been widely investigated in the literature, while the RVI (Radar Vegetation Index) that of considering volume scattering measurement has been paid relatively little attention. Also, there was only some efforts have been put to fuse the OVI with the RVI as an integrated vegetation index. To address this issue, this paper presents a novel FVI (Fusion Vegetation Index) that uses multispectral and full-PolSAR (Polarimetric Synthetic Aperture Radar) data. By fusing a NDVI (Normalized Difference Vegetation Index) of RapidEye and an RVI of C-band Radarsat-2, we demonstrated that the proposed FVI has higher separability in different vegetation types than only with OVI and RVI. Also, the experimental results show that the proposed index not only has information on the vegetation greenness of the NDVI, but also has information on the canopy structure of the RVI. Based on this preliminary result, since the vegetation monitoring is more detailed, it could be possible in various application fields; this synergistic FVI will be further developed in the future.
Multispectral Vegetation Index;Radar Vegetation Index;Fusion Vegetation Index;
 Cited by
Gao, B. (1996), NDWI-a normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sensing of Environment, Vol. 58, No. 3, pp. 257-266. crossref(new window)

Huete, A. (1988), A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, Vol. 25, No. 3, pp. 295-309. crossref(new window)

Iribe, K. and Sato, M. (2007), Analysis of polarization orientation angle shifts by artificial structures, IEEE Transactions on Geoscience and Remote Sensing, Vol. 45, No. 11, pp. 3417-3425. crossref(new window)

Jackson, R. and Huete, A. (1991), Interpreting vegetation indices. Preventive Veterinary Medicine, Vol. 11, No. 3, pp. 185-200. crossref(new window)

Kim, Y. and Van Zyl, J. (2009), A time-series approach to estimate soil moisture using polarimetric radar data, IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, No. 8, pp. 2519-2527. crossref(new window)

Kim, Y., Jackson, T., Bindlish, R., Lee, H., and Hong, S. (2012), Radar vegetation index for estimating the vegetation water content of rice and soybean, IEEE Geoscience and Remote Sensing Letters, Vol. 9, No. 4, pp. 564-568. crossref(new window)

Koppe, W., Gnyp, M., Hutt, C., Yao, Y., Miao, Y., Chen, X., and Bareth, G. (2013), Rice monitoring with multitemporal and dual-polarimetric TerraSAR-X data. International Journal of Applied Earth Observation and Geoinformation, Vol. 21, pp. 568-576. crossref(new window)

Kuenzer, C. and Knauer, K. (2013), Remote sensing of rice crop areas, International Journal of Remote Sensing, Vol. 34, No. 6, pp. 2101-2139. crossref(new window)

Lee, J.S., Grunes, M.R., and Mango, S.A. (1991), Speckle reduction in multipolarization, multifrequency SAR imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol. 29, No. 4, pp. 535-544. crossref(new window)

Matthew, M.W., Adler-Golden, S.M., Berk, A., Richtsmeier, S.C., Levine, R.Y., Bernstein, L.S., Acharya, P.K., Anderson, G.P., Felde, G.W., Hoke, M.P., Ratkowski, A., Burke, H.H., Kaiser, R.D., and Miller, D.P. (2000), Status of atmospheric correction using a MODTRAN4-based algorithm, SPIE Proceedings, Vol. 4049, pp. 199-207.

McColl, K., Entekhabi, D., and Piles, M. (2014), Uncertainy analysis of soil moisture and vegetation indices using aquarius scatterometer observations, IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 7, pp. 4259-4271. crossref(new window)

Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J., and Stenseth, N.C. (2005), Using the satellite-derived NDVI to assess ecological responses to environmental change, Trends in Ecology & Evolution, Vol. 20, No. 9, pp. 503-510. crossref(new window)

Shi, J., Jackson, T., Tao, J., Du, J., Bindlish, R., Lu, L., and Chen, K.S. (2008), Microwave vegetation indices for short vegetation covers from satellite passive microwave sensors AMSR-E, Remote Sensing of Environment, Vol. 112, No. 12, pp. 4285-4300. crossref(new window)

Zhang, L., Furumi, S., Muramatsu, K., Fujiwara, N., Daigo, M., and Zhang, L. (2007), A new vegetation index based on the universal pattern, International Journal of Remote Sensing, Vol. 21, No. 1, pp. 107-124.

Zhang, X. (2015), Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data, Remote Sensing of Environment, Vol. 156, pp. 457-472. crossref(new window)